Understanding a few terms in Andrew Ng's definition of the cost function for linear regression
Linear regression is always associated with an activation function, the weights between layers and the structure of the network. The weights between layers are $\theta_0$ and $\theta_1$. These weights and the input features undergo the dot product operation, which is then the input to the activation function of the next layer's nodes.

An apparently different but the same use of $\theta_0$ and $\theta_1$ is as coefficients to one or more number of terms which themselves are a combination of the input vectors.

Broadly, $\theta_i$ denotes a weight, i.e. how much preference you want to give to a feature.